Classification of cardiotocography records by random forest

Peterek, Tomáš; Krohová, Jana; Dohnálek, Pavel; Gajdoš, Petr

doi:10.1109/tsp.2013.6614010

Cited by 17 publications

(13 citation statements)

References 12 publications

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“…Ensemble classifiers have also shown promising performance in similar studies (Tomas et al, 2013 ; Peterek et al, 2014 ). Therefore, we also consider the AdaBoost classifier (Schapire, 1999 ) in this study as a representation of ensemble classifiers.…”

Section: Features Processingmentioning

confidence: 83%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

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Section: Features Processingmentioning

confidence: 83%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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“…It is commonly used statistical technique used for the classification. The worth of each distinct tree in not essential, the purpose of random tree is to reduce the error rate of the whole forest [22]. The error rate depends upon two factors i.e.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Shah

Habib

Aziz

et al. 2020

JPRI

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Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamics and motor activity. The classification of gait data using machine learning techniques can assist the physicians for early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree). Methodology: The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection - subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE. Results: In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected features were used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features. Conclusion: The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover, the selected features along with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible.

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“…In this study, we consider the Random Forest (RF) classifier [23], [74]. This algorithm uses an ensemble of many randomised decision-trees to vote on the classification outcome.…”

Section: Ensemble Classifiers Have Shown To Have Powerful Classificatmentioning

confidence: 99%

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces

Fergus

Selvaraj

Chalmers

2018

Computers in Biology and Medicine

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Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and intra observer variability. Incorrect decisions, caused by miss-interpretation, can lead to adverse perinatal outcomes and in severe cases death. This study presents a review of human Cardiotocography trace interpretation and argues that machine learning, used as a decision support system by obstetricians and midwives, may provide an objective measure alongside normal practices. This will help to increase predictive capacity and reduce negative outcomes. A robust methodology is presented for feature set engineering using an open database comprising 552 intrapartum recordings. State-of-the-art in signal processing techniques is applied to raw Cardiotocography foetal heart rate traces to extract 13 features. Those with low discriminative capacity are removed using Recursive Feature Elimination. The dataset is imbalanced with significant differences between the prior probabilities of both normal deliveries and those delivered by caesarean section. This issue is addressed by oversampling the training instances using a synthetic minority oversampling technique to provide a balanced class distribution. Several simple, yet powerful, machine-learning algorithms are trained, using the feature set, and their performance is evaluated with real test data. The results are encouraging using an ensemble classifier comprising Fishers Linear Discriminant Analysis, Random Forest and Support Vector Machine classifiers, with 87% (95% Confidence Interval: 86%, 88%) for Sensitivity, 90% (95% CI: 89%, 91%) for Specificity, and 96% (95% CI: 96%, 97%) for the Area Under the Curve, with a 9% (95% CI: 9%, 10%) Mean Square Error.

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Classification of cardiotocography records by random forest

Cited by 17 publications

References 12 publications

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces

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